senior-computer-vision

To deploy a YOLO object detection model using TensorRT, you export the trained model to ONNX format and use TensorRT to optimize inference for edge devices. This skill provides end-to-end workflows for this exact optimization process.

The best way to train a custom image segmentation model is using frameworks like Detectron2 or MMDetection with architectures like Mask R-CNN or SAM. This skill guides dataset preparation, model training, and architecture selection for segmentation tasks.

Yes, you can use Detectron2 with PyTorch to build object detection pipelines. This skill facilitates building detection pipelines and training custom models using frameworks like PyTorch, Ultralytics, and Detectron2 for Vision AI systems.

You prepare a custom dataset for object detection training by formatting annotations for frameworks like Ultralytics or Detectron2. This skill streamlines dataset preparation, model training, optimization, and deployment workflows for production-grade vision systems.

Yes, ONNX supports Vision Transformer architectures for inference optimization. This skill covers CNN and Vision Transformer architectures, facilitating model optimization and deployment using ONNX and TensorRT for visual AI systems.

You use TensorRT for computer vision model deployment over native PyTorch to achieve optimized inference on edge devices. This skill guides you through exporting models to ONNX and deploying them with TensorRT for production-grade vision systems.